Enhancing financial document processing: A synergetic approach with RPA and generative AI

The goal of this thesis is to improve the accuracy, speed, and efficiency of financial document processing by introducing an unconventional strategy. This work addresses the shortcomings of conventional document handling techniques by integrating sophisticated Generative Artificial Intelligence (AI) with Robotic Process Automation (RPA). The goal is to automate, therefore replace labor-intensive and error-prone operations with a more streamlined, reliable, and effective system for handling financial records. The fundamental contribution of the proposed method is the integration of the advanced generative AI capabilities a cutting-edge, with UiPath, a top RPA tool. By combining these two technologies we provide a novel approach that goes beyond the constraints of conventional manual processing. Research shows that the proposed method not only outperforms human performance in terms of efficiency but also guarantees higher accuracy and faster processing times. It is expected that the results of this study will have a significant impact on financial document management going forward. The proven effectiveness of the proposed method opens up a wide range of automation opportunities, especially in professional contexts including financial Enterprise Resource Planning (ERP) systems. In addition to contributing to the field of financial document processing, this work stimulates new possibilities for research and creativity by setting an example for the use of RPA and generative AI in other fields.